After training our model, we check that it is able to recapitulate the correct marginal distributions of dihedral and bond angles in proteins. We unconditionally generate 10 backbone chains each for every length from 50 to 128, which results in a total of 780 generated backbone chains. We plot the distributions of all six angles, aggregated across these 780 structures, and compare each distribution to that of test set structures less than 128 residues in length (Figures REF , REF ). We observe that, across all angles, the generated distribution almost exactly recapitulates the test distribution. This is true both for angles that are nearly Gaussian with low variance \((\omega , \theta _1, \theta _2, \theta _3)\)  as well as for angles with highly complex, high-variance distributions \((\phi , \psi )\) . Compared to similar plots generated from other protein diffusion methods (e.g., Figure 1 in [1]}, reproduced with permission in Figure REF ), we qualitatively observe that our method produces a much tighter distribution that more closely matches the natural distribution of bond angles.
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